"""
Retriever Agent

Implements hybrid retrieval combining dense and sparse methods.
Follows FAANG best practices for production RAG systems.

Key Features:
- Dense retrieval (embedding-based semantic search)
- Sparse retrieval (BM25/TF-IDF keyword matching)
- Reciprocal Rank Fusion (RRF) for combining results
- Query expansion using planner output
- Adaptive retrieval based on query intent
"""

from typing import List, Optional, Dict, Any, Tuple
from pydantic import BaseModel, Field
from loguru import logger
from dataclasses import dataclass
from collections import defaultdict
import re
import math

from ..store import VectorStore, VectorSearchResult, get_vector_store, VectorStoreConfig
from ..embeddings import EmbeddingAdapter, get_embedding_adapter, EmbeddingConfig
from .query_planner import QueryPlan, SubQuery, QueryIntent


class HybridSearchConfig(BaseModel):
    """Configuration for hybrid retrieval."""
    # Dense retrieval settings
    dense_weight: float = Field(default=0.7, ge=0.0, le=1.0)
    dense_top_k: int = Field(default=20, ge=1)

    # Sparse retrieval settings
    sparse_weight: float = Field(default=0.3, ge=0.0, le=1.0)
    sparse_top_k: int = Field(default=20, ge=1)

    # Fusion settings
    rrf_k: int = Field(default=60, description="RRF constant (typically 60)")
    final_top_k: int = Field(default=10, ge=1)

    # Query expansion
    use_query_expansion: bool = Field(default=True)
    max_expanded_queries: int = Field(default=3, ge=1)

    # Intent-based adaptation
    adapt_to_intent: bool = Field(default=True)


class RetrievalResult(BaseModel):
    """Result from hybrid retrieval."""
    chunk_id: str
    document_id: str
    text: str
    score: float  # Combined RRF score
    dense_score: Optional[float] = None
    sparse_score: Optional[float] = None
    dense_rank: Optional[int] = None
    sparse_rank: Optional[int] = None

    # Metadata
    page: Optional[int] = None
    chunk_type: Optional[str] = None
    source_path: Optional[str] = None
    metadata: Dict[str, Any] = Field(default_factory=dict)

    # For evidence grounding
    bbox: Optional[Dict[str, float]] = None


class RetrieverAgent:
    """
    Hybrid retrieval agent combining dense and sparse search.

    Capabilities:
    1. Dense retrieval via embedding similarity
    2. Sparse retrieval via BM25-style keyword matching
    3. Reciprocal Rank Fusion for result combination
    4. Query expansion from planner
    5. Intent-aware retrieval adaptation
    """

    def __init__(
        self,
        config: Optional[HybridSearchConfig] = None,
        vector_store: Optional[VectorStore] = None,
        embedding_adapter: Optional[EmbeddingAdapter] = None,
    ):
        """
        Initialize Retriever Agent.

        Args:
            config: Hybrid search configuration
            vector_store: Vector store for dense retrieval
            embedding_adapter: Embedding adapter for query encoding
        """
        self.config = config or HybridSearchConfig()
        self._store = vector_store
        self._embedder = embedding_adapter

        # BM25 parameters
        self._k1 = 1.5
        self._b = 0.75

        # Document statistics for BM25 (computed lazily)
        self._doc_stats: Optional[Dict[str, Any]] = None

        logger.info("RetrieverAgent initialized with hybrid search")

    @property
    def store(self) -> VectorStore:
        """Get vector store (lazy initialization)."""
        if self._store is None:
            self._store = get_vector_store()
        return self._store

    @property
    def embedder(self) -> EmbeddingAdapter:
        """Get embedding adapter (lazy initialization)."""
        if self._embedder is None:
            self._embedder = get_embedding_adapter()
        return self._embedder

    def retrieve(
        self,
        query: str,
        plan: Optional[QueryPlan] = None,
        top_k: Optional[int] = None,
        filters: Optional[Dict[str, Any]] = None,
    ) -> List[RetrievalResult]:
        """
        Perform hybrid retrieval for a query.

        Args:
            query: Search query
            plan: Optional query plan for expansion and intent
            top_k: Number of results (overrides config)
            filters: Metadata filters

        Returns:
            List of retrieval results ranked by RRF score
        """
        top_k = top_k or self.config.final_top_k

        # Get queries to run (original + expanded)
        queries = self._get_queries(query, plan)

        # Adapt retrieval based on intent
        dense_weight, sparse_weight = self._adapt_weights(plan)

        # Run dense retrieval
        dense_results = self._dense_retrieve(queries, filters)

        # Run sparse retrieval
        sparse_results = self._sparse_retrieve(queries, filters)

        # Combine with RRF
        combined = self._reciprocal_rank_fusion(
            dense_results,
            sparse_results,
            dense_weight,
            sparse_weight,
        )

        # Return top-k
        results = sorted(combined.values(), key=lambda x: x.score, reverse=True)
        return results[:top_k]

    def retrieve_for_subqueries(
        self,
        sub_queries: List[SubQuery],
        filters: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, List[RetrievalResult]]:
        """
        Retrieve for multiple sub-queries, respecting dependencies.

        Args:
            sub_queries: List of sub-queries from planner
            filters: Optional metadata filters

        Returns:
            Dict mapping sub-query ID to retrieval results
        """
        results = {}

        # Sort by priority and dependencies
        sorted_queries = self._topological_sort(sub_queries)

        for sq in sorted_queries:
            # Retrieve for this sub-query
            sq_results = self.retrieve(
                sq.query,
                top_k=self.config.final_top_k,
                filters=filters,
            )
            results[sq.id] = sq_results

        return results

    def _get_queries(
        self,
        query: str,
        plan: Optional[QueryPlan],
    ) -> List[str]:
        """Get list of queries to run (original + expanded)."""
        queries = [query]

        if plan and self.config.use_query_expansion:
            # Add expanded terms as additional queries
            for term in plan.expanded_terms[:self.config.max_expanded_queries]:
                # Combine original query with expanded term
                expanded = f"{query} {term}"
                queries.append(expanded)

        return queries

    def _adapt_weights(
        self,
        plan: Optional[QueryPlan],
    ) -> Tuple[float, float]:
        """Adapt dense/sparse weights based on query intent."""
        if not plan or not self.config.adapt_to_intent:
            return self.config.dense_weight, self.config.sparse_weight

        intent = plan.intent

        # Factoid queries benefit from keyword matching
        if intent == QueryIntent.FACTOID:
            return 0.6, 0.4

        # Definition queries benefit from semantic search
        if intent == QueryIntent.DEFINITION:
            return 0.8, 0.2

        # Comparison needs both
        if intent == QueryIntent.COMPARISON:
            return 0.5, 0.5

        # Aggregation needs broad semantic coverage
        if intent == QueryIntent.AGGREGATION:
            return 0.75, 0.25

        # List queries benefit from keyword precision
        if intent == QueryIntent.LIST:
            return 0.5, 0.5

        return self.config.dense_weight, self.config.sparse_weight

    def _dense_retrieve(
        self,
        queries: List[str],
        filters: Optional[Dict[str, Any]],
    ) -> Dict[str, Tuple[int, float]]:
        """
        Perform dense (embedding) retrieval.

        Returns:
            Dict mapping chunk_id to (rank, score)
        """
        all_results: Dict[str, List[Tuple[int, float, VectorSearchResult]]] = defaultdict(list)

        for query in queries:
            # Embed query
            query_embedding = self.embedder.embed_text(query)

            # Search
            results = self.store.search(
                query_embedding=query_embedding,
                top_k=self.config.dense_top_k,
                filters=filters,
            )

            # Record results with rank
            for rank, result in enumerate(results, 1):
                all_results[result.chunk_id].append((rank, result.similarity, result))

        # Aggregate scores across queries (take best rank/score)
        aggregated = {}
        for chunk_id, scores in all_results.items():
            best_rank = min(s[0] for s in scores)
            best_score = max(s[1] for s in scores)
            aggregated[chunk_id] = (best_rank, best_score, scores[0][2])

        return aggregated

    def _sparse_retrieve(
        self,
        queries: List[str],
        filters: Optional[Dict[str, Any]],
    ) -> Dict[str, Tuple[int, float]]:
        """
        Perform sparse (BM25-style) retrieval.

        Returns:
            Dict mapping chunk_id to (rank, score)
        """
        # Get all chunks from vector store for sparse search
        # In production, this would use an inverted index
        try:
            all_chunks = self._get_all_chunks(filters)
        except Exception as e:
            logger.warning(f"Sparse retrieval failed: {e}")
            return {}

        if not all_chunks:
            return {}

        # Compute document statistics if needed
        if self._doc_stats is None:
            self._compute_doc_stats(all_chunks)

        # Score all chunks for each query
        all_scores: Dict[str, List[float]] = defaultdict(list)

        for query in queries:
            query_terms = self._tokenize(query)
            for chunk_id, text in all_chunks.items():
                score = self._bm25_score(query_terms, text)
                all_scores[chunk_id].append(score)

        # Aggregate scores (take max)
        aggregated = {}
        for chunk_id, scores in all_scores.items():
            best_score = max(scores)
            aggregated[chunk_id] = best_score

        # Rank by score
        ranked = sorted(aggregated.items(), key=lambda x: x[1], reverse=True)
        result = {}
        for rank, (chunk_id, score) in enumerate(ranked[:self.config.sparse_top_k], 1):
            result[chunk_id] = (rank, score, None)

        return result

    def _get_all_chunks(
        self,
        filters: Optional[Dict[str, Any]],
    ) -> Dict[str, str]:
        """Get all chunks for sparse retrieval."""
        # This is a simplified implementation
        # In production, use an inverted index

        # Get chunk IDs from dense search with generic query
        query_embedding = self.embedder.embed_text("document content information")
        results = self.store.search(
            query_embedding=query_embedding,
            top_k=1000,  # Get as many as possible
            filters=filters,
        )

        chunks = {}
        for result in results:
            chunks[result.chunk_id] = result.text

        return chunks

    def _compute_doc_stats(self, chunks: Dict[str, str]):
        """Compute document statistics for BM25."""
        doc_lengths = []
        df = defaultdict(int)  # Document frequency

        for text in chunks.values():
            terms = self._tokenize(text)
            doc_lengths.append(len(terms))
            for term in set(terms):
                df[term] += 1

        self._doc_stats = {
            "avg_dl": sum(doc_lengths) / len(doc_lengths) if doc_lengths else 1,
            "n_docs": len(chunks),
            "df": dict(df),
        }

    def _tokenize(self, text: str) -> List[str]:
        """Simple tokenization."""
        text = text.lower()
        text = re.sub(r'[^\w\s]', ' ', text)
        return text.split()

    def _bm25_score(self, query_terms: List[str], doc_text: str) -> float:
        """Compute BM25 score."""
        if not self._doc_stats:
            return 0.0

        doc_terms = self._tokenize(doc_text)
        dl = len(doc_terms)
        avg_dl = self._doc_stats["avg_dl"]
        n_docs = self._doc_stats["n_docs"]
        df = self._doc_stats["df"]

        # Count term frequencies in document
        tf = defaultdict(int)
        for term in doc_terms:
            tf[term] += 1

        score = 0.0
        for term in query_terms:
            if term not in tf:
                continue

            # IDF
            doc_freq = df.get(term, 0)
            idf = math.log((n_docs - doc_freq + 0.5) / (doc_freq + 0.5) + 1)

            # TF with saturation
            term_freq = tf[term]
            tf_component = (term_freq * (self._k1 + 1)) / (
                term_freq + self._k1 * (1 - self._b + self._b * dl / avg_dl)
            )

            score += idf * tf_component

        return score

    def _reciprocal_rank_fusion(
        self,
        dense_results: Dict[str, Tuple[int, float, Any]],
        sparse_results: Dict[str, Tuple[int, float, Any]],
        dense_weight: float,
        sparse_weight: float,
    ) -> Dict[str, RetrievalResult]:
        """
        Combine dense and sparse results using RRF.

        RRF score = sum(1 / (k + rank)) for each ranking
        """
        k = self.config.rrf_k
        combined = {}

        # Get all unique chunk IDs
        all_chunk_ids = set(dense_results.keys()) | set(sparse_results.keys())

        for chunk_id in all_chunk_ids:
            dense_rank = dense_results.get(chunk_id, (1000, 0, None))[0]
            dense_score = dense_results.get(chunk_id, (1000, 0, None))[1]
            sparse_rank = sparse_results.get(chunk_id, (1000, 0, None))[0]
            sparse_score = sparse_results.get(chunk_id, (1000, 0, None))[1]

            # RRF formula
            rrf_dense = dense_weight / (k + dense_rank) if chunk_id in dense_results else 0
            rrf_sparse = sparse_weight / (k + sparse_rank) if chunk_id in sparse_results else 0
            rrf_score = rrf_dense + rrf_sparse

            # Get metadata from dense results if available
            metadata = {}
            page = None
            chunk_type = None
            source_path = None
            text = ""
            document_id = ""
            bbox = None

            if chunk_id in dense_results:
                result_obj = dense_results[chunk_id][2]
                if result_obj:
                    text = result_obj.text
                    document_id = result_obj.document_id
                    page = result_obj.page
                    chunk_type = result_obj.chunk_type
                    metadata = result_obj.metadata
                    source_path = metadata.get("source_path", "")
                    bbox = result_obj.bbox

            combined[chunk_id] = RetrievalResult(
                chunk_id=chunk_id,
                document_id=document_id,
                text=text,
                score=rrf_score,
                dense_score=dense_score if chunk_id in dense_results else None,
                sparse_score=sparse_score if chunk_id in sparse_results else None,
                dense_rank=dense_rank if chunk_id in dense_results else None,
                sparse_rank=sparse_rank if chunk_id in sparse_results else None,
                page=page,
                chunk_type=chunk_type,
                source_path=source_path,
                metadata=metadata,
                bbox=bbox,
            )

        return combined

    def _topological_sort(self, sub_queries: List[SubQuery]) -> List[SubQuery]:
        """Sort sub-queries by dependencies."""
        # Simple topological sort
        sorted_queries = []
        remaining = list(sub_queries)
        completed = set()

        while remaining:
            for sq in remaining[:]:
                if all(dep in completed for dep in sq.depends_on):
                    sorted_queries.append(sq)
                    completed.add(sq.id)
                    remaining.remove(sq)
                    break
            else:
                # Cycle detected or invalid dependencies, just append rest
                sorted_queries.extend(remaining)
                break

        return sorted_queries